Overview

Elephant & Rider


Age


Logistic Regression

## 
## Call:  glm(formula = elephant_type ~ D1, family = binomial, data = ele_plot)
## 
## Coefficients:
## (Intercept)           D1  
##     2.11717     -0.01268  
## 
## Degrees of Freedom: 257 Total (i.e. Null);  256 Residual
##   (17 observations deleted due to missingness)
## Null Deviance:       222.6 
## Residual Deviance: 221.2     AIC: 225.2
Statistical models
Model 1
(Intercept) 2.12***
(0.40)
D1 -0.01
(0.01)
AIC 225.17
BIC 232.27
Log Likelihood -110.58
Deviance 221.17
Num. obs. 258
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = elephant_type ~ ppol_num, family = binomial, data = ele_plot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##      2.1423      -0.1157  
## 
## Degrees of Freedom: 215 Total (i.e. Null);  214 Residual
##   (59 observations deleted due to missingness)
## Null Deviance:       181.2 
## Residual Deviance: 180.4     AIC: 184.4
Statistical models
Model 1
(Intercept) 2.14***
(0.48)
ppol_num -0.12
(0.13)
AIC 184.38
BIC 191.13
Log Likelihood -90.19
Deviance 180.38
Num. obs. 216
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType

Environment/Economy example


Age


Logistic Regression

## 
## Call:  glm(formula = env_type ~ D1, family = binomial, data = env_plot)
## 
## Coefficients:
## (Intercept)           D1  
##   1.1585702   -0.0007942  
## 
## Degrees of Freedom: 171 Total (i.e. Null);  170 Residual
##   (7 observations deleted due to missingness)
## Null Deviance:       191.2 
## Residual Deviance: 191.2     AIC: 195.2
Statistical models
Model 1
(Intercept) 1.16**
(0.45)
D1 -0.00
(0.01)
AIC 195.21
BIC 201.51
Log Likelihood -95.61
Deviance 191.21
Num. obs. 172
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = env_type ~ ppol_num, family = binomial, data = env_plot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##     1.30692     -0.05042  
## 
## Degrees of Freedom: 140 Total (i.e. Null);  139 Residual
##   (38 observations deleted due to missingness)
## Null Deviance:       155.8 
## Residual Deviance: 155.6     AIC: 159.6
Statistical models
Model 1
(Intercept) 1.31**
(0.48)
ppol_num -0.05
(0.14)
AIC 159.64
BIC 165.53
Log Likelihood -77.82
Deviance 155.64
Num. obs. 141
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType

Trolley


Age


Logistic Regression

## 
## Call:  glm(formula = trolley_type ~ D1, family = binomial, data = trolley_plot)
## 
## Coefficients:
## (Intercept)           D1  
##     2.58512     -0.03659  
## 
## Degrees of Freedom: 89 Total (i.e. Null);  88 Residual
##   (6 observations deleted due to missingness)
## Null Deviance:       95.35 
## Residual Deviance: 89.42     AIC: 93.42
Statistical models
Model 1
(Intercept) 2.59***
(0.64)
D1 -0.04*
(0.02)
AIC 93.42
BIC 98.42
Log Likelihood -44.71
Deviance 89.42
Num. obs. 90
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = trolley_type ~ ppol_num, family = binomial, data = trolley_plot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##      0.7612       0.1577  
## 
## Degrees of Freedom: 74 Total (i.e. Null);  73 Residual
##   (21 observations deleted due to missingness)
## Null Deviance:       80.28 
## Residual Deviance: 79.49     AIC: 83.49
Statistical models
Model 1
(Intercept) 0.76
(0.58)
ppol_num 0.16
(0.18)
AIC 83.49
BIC 88.12
Log Likelihood -39.74
Deviance 79.49
Num. obs. 75
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType

Self Reflection


Age


Logistic Regression

## 
## Call:  glm(formula = reflection_type ~ D1, family = binomial, data = reflection_plot)
## 
## Coefficients:
## (Intercept)           D1  
##    1.005838    -0.003409  
## 
## Degrees of Freedom: 105 Total (i.e. Null);  104 Residual
##   (5 observations deleted due to missingness)
## Null Deviance:       128.1 
## Residual Deviance: 128   AIC: 132
Statistical models
Model 1
(Intercept) 1.01*
(0.50)
D1 -0.00
(0.01)
AIC 132.04
BIC 137.37
Log Likelihood -64.02
Deviance 128.04
Num. obs. 106
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = reflection_type ~ ppol_num, family = binomial, 
##     data = reflection_plot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##      0.4691       0.1599  
## 
## Degrees of Freedom: 79 Total (i.e. Null);  78 Residual
##   (31 observations deleted due to missingness)
## Null Deviance:       94.11 
## Residual Deviance: 93.21     AIC: 97.21
Statistical models
Model 1
(Intercept) 0.47
(0.58)
ppol_num 0.16
(0.17)
AIC 97.21
BIC 101.98
Log Likelihood -46.61
Deviance 93.21
Num. obs. 80
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType

Post Hoc


Age


Logistic Regression

## 
## Call:  glm(formula = posthoc_type ~ D1, family = binomial, data = posthoc_plot)
## 
## Coefficients:
## (Intercept)           D1  
##     1.55012      0.03725  
## 
## Degrees of Freedom: 63 Total (i.e. Null);  62 Residual
##   (2 observations deleted due to missingness)
## Null Deviance:       29.93 
## Residual Deviance: 28.85     AIC: 32.85
Statistical models
Model 1
(Intercept) 1.55
(1.23)
D1 0.04
(0.04)
AIC 32.85
BIC 37.16
Log Likelihood -14.42
Deviance 28.85
Num. obs. 64
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = posthoc_type ~ ppol_num, family = binomial, data = posthoc_plot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##     2.54424      0.06768  
## 
## Degrees of Freedom: 49 Total (i.e. Null);  48 Residual
##   (16 observations deleted due to missingness)
## Null Deviance:       22.7 
## Residual Deviance: 22.67     AIC: 26.67
Statistical models
Model 1
(Intercept) 2.54
(1.45)
ppol_num 0.07
(0.44)
AIC 26.67
BIC 30.50
Log Likelihood -11.34
Deviance 22.67
Num. obs. 50
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType

Psych


Age


Logistic Regression

## 
## Call:  glm(formula = psych_type ~ D1, family = binomial, data = psych_plot)
## 
## Coefficients:
## (Intercept)           D1  
##     3.22935     -0.02344  
## 
## Degrees of Freedom: 61 Total (i.e. Null);  60 Residual
##   (8 observations deleted due to missingness)
## Null Deviance:       34.76 
## Residual Deviance: 34.03     AIC: 38.03
Statistical models
Model 1
(Intercept) 3.23**
(1.08)
D1 -0.02
(0.03)
AIC 38.03
BIC 42.28
Log Likelihood -17.01
Deviance 34.03
Num. obs. 62
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = psych_type ~ ppol_num, family = binomial, data = psych_plot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##      0.8997       0.2857  
## 
## Degrees of Freedom: 51 Total (i.e. Null);  50 Residual
##   (18 observations deleted due to missingness)
## Null Deviance:       41.09 
## Residual Deviance: 40.32     AIC: 44.32
Statistical models
Model 1
(Intercept) 0.90
(1.16)
ppol_num 0.29
(0.34)
AIC 44.32
BIC 48.22
Log Likelihood -20.16
Deviance 40.32
Num. obs. 52
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType

Confirmation Bias


Age


Logistic Regression

## 
## Call:  glm(formula = confbias_type ~ D1, family = binomial, data = confbias_plot)
## 
## Coefficients:
## (Intercept)           D1  
##   3.734e+00    8.156e-05  
## 
## Degrees of Freedom: 42 Total (i.e. Null);  41 Residual
##   (2 observations deleted due to missingness)
## Null Deviance:       9.499 
## Residual Deviance: 9.499     AIC: 13.5
Statistical models
Model 1
(Intercept) 3.73
(2.58)
D1 0.00
(0.06)
AIC 13.50
BIC 17.02
Log Likelihood -4.75
Deviance 9.50
Num. obs. 43
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories

Error in stats::fisher.test(ftab, simulate.p.value = (nrow(ftab) > 2 || : need 2 or more non-zero row marginals


Progressives vs. Conservatives


Logistic Regression

only 1 dislike so this doesn’t work




Race/Ethnicity


UserType

Not New


Age


Logistic Regression

## 
## Call:  glm(formula = notnew_type ~ D1, family = binomial, data = notnew_plot)
## 
## Coefficients:
## (Intercept)           D1  
##    0.703483     0.000862  
## 
## Degrees of Freedom: 36 Total (i.e. Null);  35 Residual
##   (4 observations deleted due to missingness)
## Null Deviance:       46.63 
## Residual Deviance: 46.62     AIC: 50.62
Statistical models
Model 1
(Intercept) 0.70
(0.84)
D1 0.00
(0.02)
AIC 50.62
BIC 53.85
Log Likelihood -23.31
Deviance 46.62
Num. obs. 37
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = notnew_type ~ ppol_num, family = binomial, data = notnew_plot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##     1.07298     -0.02354  
## 
## Degrees of Freedom: 29 Total (i.e. Null);  28 Residual
##   (11 observations deleted due to missingness)
## Null Deviance:       34.79 
## Residual Deviance: 34.79     AIC: 38.79
Statistical models
Model 1
(Intercept) 1.07
(0.99)
ppol_num -0.02
(0.34)
AIC 38.79
BIC 41.59
Log Likelihood -17.40
Deviance 34.79
Num. obs. 30
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType

Examples


Age


Logistic Regression

## 
## Call:  glm(formula = examplestype ~ D1, family = binomial, data = examplesplot)
## 
## Coefficients:
## (Intercept)           D1  
##   -0.496460    -0.004402  
## 
## Degrees of Freedom: 66 Total (i.e. Null);  65 Residual
##   (4 observations deleted due to missingness)
## Null Deviance:       86.19 
## Residual Deviance: 86.1  AIC: 90.1
Statistical models
Model 1
(Intercept) -0.50
(0.58)
D1 -0.00
(0.02)
AIC 90.10
BIC 94.51
Log Likelihood -43.05
Deviance 86.10
Num. obs. 67
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = examplestype ~ ppol_num, family = binomial, data = examplesplot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##     -1.5439       0.2273  
## 
## Degrees of Freedom: 57 Total (i.e. Null);  56 Residual
##   (13 observations deleted due to missingness)
## Null Deviance:       71.85 
## Residual Deviance: 70.05     AIC: 74.05
Statistical models
Model 1
(Intercept) -1.54*
(0.64)
ppol_num 0.23
(0.17)
AIC 74.05
BIC 78.17
Log Likelihood -35.02
Deviance 70.05
Num. obs. 58
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType

Questions


Age


Logistic Regression

## 
## Call:  glm(formula = questions_type ~ D1, family = binomial, data = questions_plot)
## 
## Coefficients:
## (Intercept)           D1  
##    0.019810    -0.007471  
## 
## Degrees of Freedom: 37 Total (i.e. Null);  36 Residual
##   (2 observations deleted due to missingness)
## Null Deviance:       52.26 
## Residual Deviance: 52.16     AIC: 56.16
Statistical models
Model 1
(Intercept) 0.02
(0.80)
D1 -0.01
(0.02)
AIC 56.16
BIC 59.43
Log Likelihood -26.08
Deviance 52.16
Num. obs. 38
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = questions_type ~ ppol_num, family = binomial, data = questions_plot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##      1.1605      -0.3304  
## 
## Degrees of Freedom: 27 Total (i.e. Null);  26 Residual
##   (12 observations deleted due to missingness)
## Null Deviance:       38.67 
## Residual Deviance: 37.36     AIC: 41.36
Statistical models
Model 1
(Intercept) 1.16
(1.00)
ppol_num -0.33
(0.30)
AIC 41.36
BIC 44.03
Log Likelihood -18.68
Deviance 37.36
Num. obs. 28
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType

Explanation


Age


Logistic Regression

## 
## Call:  glm(formula = explanation_type ~ D1, family = binomial, data = explanation_plot)
## 
## Coefficients:
## (Intercept)           D1  
##    -2.17976      0.03623  
## 
## Degrees of Freedom: 27 Total (i.e. Null);  26 Residual
##   (4 observations deleted due to missingness)
## Null Deviance:       35.16 
## Residual Deviance: 32.82     AIC: 36.82
Statistical models
Model 1
(Intercept) -2.18*
(1.08)
D1 0.04
(0.02)
AIC 36.82
BIC 39.48
Log Likelihood -16.41
Deviance 32.82
Num. obs. 28
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = explanation_type ~ ppol_num, family = binomial, 
##     data = explanation_plot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##     -0.4910      -0.2131  
## 
## Degrees of Freedom: 20 Total (i.e. Null);  19 Residual
##   (11 observations deleted due to missingness)
## Null Deviance:       23.05 
## Residual Deviance: 22.8  AIC: 26.8
Statistical models
Model 1
(Intercept) -0.49
(1.42)
ppol_num -0.21
(0.43)
AIC 26.80
BIC 28.89
Log Likelihood -11.40
Deviance 22.80
Num. obs. 21
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType

Psych Exercises


Age


Logistic Regression

## 
## Call:  glm(formula = psychexercises_type ~ D1, family = binomial, data = psychexercises_plot)
## 
## Coefficients:
## (Intercept)           D1  
##    -1.86366      0.08672  
## 
## Degrees of Freedom: 15 Total (i.e. Null);  14 Residual
## Null Deviance:       19.87 
## Residual Deviance: 16.15     AIC: 20.15
Statistical models
Model 1
(Intercept) -1.86
(1.73)
D1 0.09
(0.06)
AIC 20.15
BIC 21.69
Log Likelihood -8.07
Deviance 16.15
Num. obs. 16
p < 0.001, p < 0.01, p < 0.05




Gender



Political Orientation


Seven Categories


Progressives vs. Conservatives


Logistic Regression

## 
## Call:  glm(formula = psychexercises_type ~ ppol_num, family = binomial, 
##     data = psychexercises_plot)
## 
## Coefficients:
## (Intercept)     ppol_num  
##     0.79858     -0.05647  
## 
## Degrees of Freedom: 13 Total (i.e. Null);  12 Residual
##   (2 observations deleted due to missingness)
## Null Deviance:       18.25 
## Residual Deviance: 18.22     AIC: 22.22
Statistical models
Model 1
(Intercept) 0.80
(1.47)
ppol_num -0.06
(0.36)
AIC 22.22
BIC 23.50
Log Likelihood -9.11
Deviance 18.22
Num. obs. 14
p < 0.001, p < 0.01, p < 0.05




Race/Ethnicity


UserType